The 2016-2021 World Outlook for 32 MW Grid-Connected PV Solar Photovoltaic Systems

1 INTRODUCTION 10
1.1 Overview 10
1.2 What is Latent Demand and the P.I.E.? 10
1.3 The Methodology 11
1.3.1 Step 1. Product Definition and Data Collection 12
1.3.2 Step 2. Filtering and Smoothing 13
1.3.3 Step 3. Filling in Missing Values 13
1.3.4 Step 4. Varying Parameter, Non-linear Estimation 13
1.3.5 Step 5. Fixed-Parameter Linear Estimation 14
1.3.6 Step 6. Aggregation and Benchmarking 14
1.3.7 Step 7. Latent Demand Density: Allocating Across Cities 14
1.4 Frequently Asked Questions (FAQ) 15
1.4.1 Category Definition 15
1.4.2 Units 15
1.4.3 Methodology 16
2 SUMMARY OF FINDINGS 18
2.1 The Worldwide Market Potential 18
3 AFRICA & THE MIDDLE EAST 20
3.1 Executive Summary 20
3.2 Afghanistan 22
3.3 Algeria 23
3.4 Angola 25
3.5 Armenia 25
3.6 Azerbaijan 26
3.7 Bahrain 27
3.8 Benin 28
3.9 Botswana 29
3.10 Burkina Faso 30
3.11 Burundi 31
3.12 Cameroon 31
3.13 Cape Verde 32
3.14 Central African Republic 33
3.15 Chad 33
3.16 Comoros 34
3.17 Congo (formerly Zaire) 35
3.18 Cote d'Ivoire 36
3.19 Djibouti 36
3.20 Egypt 37
3.21 Equatorial Guinea 38
3.22 Eritrea 39
3.23 Ethiopia 40
3.24 Gabon 41
3.25 Ghana 41
3.26 Guinea 43
3.27 Guinea-Bissau 43
3.28 Iran 44
3.29 Iraq 47
3.30 Israel 48
3.31 Jordan 49
3.32 Kenya 49
3.33 Kuwait 51
3.34 Kyrgyzstan 52
3.35 Lebanon 53
3.36 Lesotho 53
3.37 Liberia 54
3.38 Libya 55
3.39 Madagascar 56
3.40 Malawi 56
3.41 Mali 57
3.42 Mauritania 58
3.43 Mauritius 58
3.44 Morocco 59
3.45 Mozambique 60
3.46 Namibia 61
3.47 Niger 62
3.48 Nigeria 63
3.49 North Sudan 65
3.50 Oman 66
3.51 Pakistan 67
3.52 Palestine 69
3.53 Qatar 70
3.54 Republic of Congo 71
3.55 Rwanda 71
3.56 Sao Tome E Principe 72
3.57 Saudi Arabia 73
3.58 Senegal 74
3.59 Sierra Leone 75
3.60 Somalia 75
3.61 South Africa 76
3.62 South Sudan 77
3.63 Swaziland 78
3.64 Syrian Arab Republic 78
3.65 Tajikistan 80
3.66 Tanzania 80
3.67 The Gambia 81
3.68 The United Arab Emirates 82
3.69 Togo 83
3.70 Tunisia 84
3.71 Turkey 85
3.72 Turkmenistan 88
3.73 Uganda 89
3.74 Uzbekistan 90
3.75 Western Sahara 91
3.76 Yemen 91
3.77 Zambia 92
3.78 Zimbabwe 93
4 ASIA 94
4.1 Executive Summary 94
4.2 Bangladesh 96
4.3 Bhutan 98
4.4 Brunei 98
4.5 Burma 99
4.6 Cambodia 100
4.7 China 100
4.8 Hong Kong 102
4.9 India 103
4.10 Indonesia 105
4.11 Japan 107
4.12 Laos 110
4.13 Macau 111
4.14 Malaysia 112
4.15 Maldives 113
4.16 Mongolia 113
4.17 Nepal 114
4.18 North Korea 115
4.19 Papua New Guinea 116
4.20 Philippines 117
4.21 Seychelles 121
4.22 Singapore 122
4.23 South Korea 122
4.24 Sri Lanka 124
4.25 Taiwan 124
4.26 Thailand 125
4.27 Timor - Leste, Democratic Republic of 126
4.28 Vietnam 127
5 EUROPE 128
5.1 Executive Summary 128
5.2 Albania 130
5.3 Andorra 131
5.4 Austria 131
5.5 Belarus 132
5.6 Belgium 133
5.7 Bosnia and Herzegovina 134
5.8 Bulgaria 135
5.9 Croatia 136
5.10 Cyprus 136
5.11 Czech Republic 137
5.12 Denmark 138
5.13 Estonia 139
5.14 Finland 140
5.15 France 141
5.16 Georgia 143
5.17 Germany 143
5.18 Greece 144
5.19 Hungary 146
5.20 Iceland 147
5.21 Ireland 148
5.22 Italy 148
5.23 Kazakhstan 150
5.24 Kosovo 152
5.25 Latvia 152
5.26 Liechtenstein 153
5.27 Lithuania 154
5.28 Luxembourg 154
5.29 Macedonia 155
5.30 Malta 156
5.31 Moldova 156
5.32 Monaco 157
5.33 Montenegro 158
5.34 Norway 158
5.35 Poland 159
5.36 Portugal 161
5.37 Romania 162
5.38 Russia 163
5.39 San Marino 166
5.40 Serbia 166
5.41 Slovakia 167
5.42 Slovenia 168
5.43 Spain 169
5.44 Sweden 171
5.45 Switzerland 172
5.46 The Netherlands 173
5.47 The United Kingdom 175
5.48 Ukraine 177
6 LATIN AMERICA 180
6.1 Executive Summary 180
6.2 Argentina 182
6.3 Belize 185
6.4 Bolivia 186
6.5 Brazil 187
6.6 Chile 190
6.7 Colombia 191
6.8 Costa Rica 193
6.9 Ecuador 194
6.10 El Salvador 195
6.11 Guatemala 196
6.12 Guyana 197
6.13 Honduras 197
6.14 Mexico 198
6.15 Nicaragua 200
6.16 Panama 201
6.17 Paraguay 201
6.18 Peru 202
6.19 Suriname 203
6.20 The Falkland Islands 204
6.21 Uruguay 205
6.22 Venezuela 206
7 NORTH AMERICA & THE CARIBBEAN 208
7.1 Executive Summary 208
7.2 Antigua and Barbuda 210
7.3 Aruba 210
7.4 Barbados 211
7.5 Bermuda 212
7.6 Canada 212
7.7 Cuba 214
7.8 Dominica 215
7.9 Dominican Republic 216
7.10 Greenland 217
7.11 Grenada 218
7.12 Haiti 218
7.13 Jamaica 219
7.14 Puerto Rico 220
7.15 St. Kitts and Nevis 221
7.16 St. Lucia 221
7.17 St. Vincent and the Grenadines 222
7.18 The Bahamas 223
7.19 The British Virgin Islands 223
7.20 The Cayman Islands 224
7.21 The U.S. Virgin Islands 225
7.22 The United States 225
7.23 Trinidad and Tobago 227
8 OCEANA 228
8.1 Executive Summary 228
8.2 American Samoa 229
8.3 Australia 230
8.4 Christmas Island 231
8.5 Cook Islands 231
8.6 Fiji 232
8.7 French Polynesia 232
8.8 Guam 233
8.9 Kiribati 234
8.10 Marshall Islands 234
8.11 Micronesia Federation 235
8.12 Nauru 235
8.13 New Caledonia 236
8.14 New Zealand 237
8.15 Niue 238
8.16 Norfolk Island 238
8.17 Palau 239
8.18 Solomon Islands 239
8.19 The Northern Mariana Island 240
8.20 Tonga 241
8.21 Tuvalu 241
8.22 Vanuatu 242
8.23 Wallis and Futuna 242
8.24 Western Samoa 243
9 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 244
9.1 Disclaimers & Safe Harbor 244
9.2 ICON Group International, Inc. User Agreement Provisions 245


WHAT IS LATENT DEMAND AND THE P.I.E.?

The concept of latent demand is rather subtle. The term latent typically refers to something that is dormant, not observable, or not yet realized. Demand is the notion of an economic quantity that a target population or market requires under different assumptions of price, quality, and distribution, among other factors. Latent demand, therefore, is commonly defined by economists as the industry earnings of a market when that market becomes accessible and attractive to serve by competing firms. It is a measure, therefore, of potential industry earnings (P.I.E.) or total revenues (not profit) if a market is served in an efficient manner. It is typically expressed as the total revenues potentially extracted by firms. The “market” is defined at a given level in the value chain. There can be latent demand at the retail level, at the wholesale level, the manufacturing level, and the raw materials level (the P.I.E. of higher levels of the value chain being always smaller than the P.I.E. of levels at lower levels of the same value chain, assuming all levels maintain minimum profitability).

The latent demand for 32 MW grid-connected PV solar photovoltaic systems is not actual or historic sales. Nor is latent demand future sales. In fact, latent demand can be lower either lower or higher than actual sales if a market is inefficient (i.e., not representative of relatively competitive levels). Inefficiencies arise from a number of factors, including the lack of international openness, cultural barriers to consumption, regulations, and cartel-like behavior on the part of firms. In general, however, latent demand is typically larger than actual sales in a country market.

For reasons discussed later, this report does not consider the notion of “unit quantities”, only total latent revenues (i.e., a calculation of price times quantity is never made, though one is implied). The units used in this report are U.S. dollars not adjusted for inflation (i.e., the figures incorporate inflationary trends) and not adjusted for future dynamics in exchange rates. If inflation rates or exchange rates vary in a substantial way compared to recent experience, actually sales can also exceed latent demand (when expressed in U.S. dollars, not adjusted for inflation). On the other hand, latent demand can be typically higher than actual sales as there are often distribution inefficiencies that reduce actual sales below the level of latent demand.

As mentioned in the introduction, this study is strategic in nature, taking an aggregate and long-run view, irrespective of the players or products involved. If fact, all the current products or services on the market can cease to exist in their present form (i.e., at a brand-, R&D specification, or corporate-image level) and all the players can be replaced by other firms (i.e., via exits, entries, mergers, bankruptcies, etc.), and there will still be an international latent demand for 32 MW grid-connected PV solar photovoltaic systems at the aggregate level. Product and service offering details, and the actual identity of the players involved, while important for certain issues, are relatively unimportant for estimates of latent demand.

THE METHODOLOGY

In order to estimate the latent demand for 32 MW grid-connected PV solar photovoltaic systems on a worldwide basis, I used a multi-stage approach. Before applying the approach, one needs a basic theory from which such estimates are created. In this case, I heavily rely on the use of certain basic economic assumptions. In particular, there is an assumption governing the shape and type of aggregate latent demand functions. Latent demand functions relate the income of a country, city, state, household, or individual to realized consumption. Latent demand (often realized as consumption when an industry is efficient), at any level of the value chain, takes place if an equilibrium is realized. For firms to serve a market, they must perceive a latent demand and be able to serve that demand at a minimal return. The single most important variable determining consumption, assuming latent demand exists, is income (or other financial resources at higher levels of the value chain). Other factors that can pivot or shape demand curves include external or exogenous shocks (i.e., business cycles), and or changes in utility for the product in question.

Ignoring, for the moment, exogenous shocks and variations in utility across countries, the aggregate relation between income and consumption has been a central theme in economics. The figure below concisely summarizes one aspect of problem. In the 1930s, John Meynard Keynes conjectured that as incomes rise, the average propensity to consume would fall. The average propensity to consume is the level of consumption divided by the level of income, or the slope of the line from the origin to the consumption function. He estimated this relationship empirically and found it to be true in the short-run (mostly based on cross-sectional data). The higher the income, the lower the average propensity to consume. This type of consumption function is labeled "A" in the figure below (note the rather flat slope of the curve). In the 1940s, another macroeconomist, Simon Kuznets, estimated long-run consumption functions which indicated that the marginal propensity to consume was rather constant (using time series data across countries). This type of consumption function is show as "B" in the figure below (note the higher slope and zero-zero intercept). The average propensity to consume is constant.















Is it declining or is it constant? A number of other economists, notably Franco Modigliani and Milton Friedman, in the 1950s (and Irving Fisher earlier), explained why the two functions were different using various assumptions on intertemporal budget constraints, savings, and wealth. The shorter the time horizon, the more consumption can depend on wealth (earned in previous years) and business cycles. In the long-run, however, the propensity to consume is more constant. Similarly, in the long run, households, industries or countries with no income eventually have no consumption (wealth is depleted). While the debate surrounding beliefs about how income and consumption are related and interesting, in this study a very particular school of thought is adopted. In particular, we are considering the latent demand for 32 MW grid-connected PV solar photovoltaic systems across some 230 countries. The smallest have fewer than 10,000 inhabitants. I assume that all of these counties fall along a "long-run" aggregate consumption function. This long-run function applies despite some of these countries having wealth, current income dominates the latent demand for 32 MW grid-connected PV solar photovoltaic systems. So, latent demand in the long-run has a zero intercept. However, I allow firms to have different propensities to consume (including being on consumption functions with differing slopes, which can account for differences in industrial organization, and end-user preferences).

Given this overriding philosophy, I will now describe the methodology used to create the latent demand estimates for 32 MW grid-connected PV solar photovoltaic systems. Since ICON Group has asked me to apply this methodology to a large number of categories, the rather academic discussion below is general and can be applied to a wide variety of categories, not just 32 MW grid-connected PV solar photovoltaic systems.

Step 1. Product Definition and Data Collection

Any study of latent demand across countries requires that some standard be established to define “efficiently served”. Having implemented various alternatives and matched these with market outcomes, I have found that the optimal approach is to assume that certain key countries are more likely to be at or near efficiency than others. These countries are given greater weight than others in the estimation of latent demand compared to other countries for which no known data are available. Of the many alternatives, I have found the assumption that the world’s highest aggregate income and highest income-per-capita markets reflect the best standards for “efficiency”. High aggregate income alone is not sufficient (i.e., China has high aggregate income, but low income per capita and can not assumed to be efficient). Aggregate income can be operationalized in a number of ways, including gross domestic product (for industrial categories), or total disposable income (for household categories; population times average income per capita, or number of households times average household income per capita). Brunei, Nauru, Kuwait, and Lichtenstein are examples of countries with high income per capita, but not assumed to be efficient, given low aggregate level of income (or gross domestic product); these countries have, however, high incomes per capita but may not benefit from the efficiencies derived from economies of scale associated with large economies. Only countries with high income per capita and large aggregate income are assumed efficient. This greatly restricts the pool of countries to those in the OECD (Organization for Economic Cooperation and Development), like the United States, or the United Kingdom (which were earlier than other large OECD economies to liberalize their markets).

The selection of countries is further reduced by the fact that not all countries in the OECD report industry revenues at the category level. Countries that typically have ample data at the aggregate level that meet the efficiency criteria include the United States, the United Kingdom and in some cases France and Germany.

Latent demand is therefore estimated using data collected for relatively efficient markets from independent data sources (e.g. Euromonitor, Mintel, Thomson Financial Services, the U.S. Industrial Outlook, the World Resources Institute, the Organization for Economic Cooperation and Development, various agencies from the United Nations, industry trade associations, the International Monetary Fund, and the World Bank). Depending on original data sources used, the definition of “32 MW grid-connected PV solar photovoltaic systems” is established. In the case of this report, the data were reported at the aggregate level, with no further breakdown or definition. In other words, any potential product or service that might be incorporated within 32 MW grid-connected PV solar photovoltaic systems falls under this category. Public sources rarely report data at the disaggregated level in order to protect private information from individual firms that might dominate a specific product-market. These sources will therefore aggregate across components of a category and report only the aggregate to the public. While private data are certainly available, this report only relies on public data at the aggregate level without reliance on the summation of various category components. In other words, this report does not aggregate a number of components to arrive at the “whole”. Rather, it starts with the “whole”, and estimates the whole for all countries and the world at large (without needing to know the specific parts that went into the whole in the first place).

Given this caveat, in this report we define the sales of "32 MW grid-connected PV solar photovoltaic systems" as including all commonly understood products falling within this broad category. Companies participating in this industry include BP Solarex, Shell Solar, and Evergreen Solar. All figures are in a common currency (U.S. dollars, millions) and are not adjusted for inflation (i.e., they are current values). Exchange rates used to convert to U.S. dollars are averages for the year in question. Future exchange rates are assumed to be constant in the future at the current level (the average of the year of this publication’s release in 2015).

Step 2. Filtering and Smoothing

Based on the aggregate view of 32 MW grid-connected PV solar photovoltaic systems as defined above, data were then collected for as many similar countries as possible for that same definition, at the same level of the value chain. This generates a convenience sample of countries from which comparable figures are available. If the series in question do not reflect the same accounting period, then adjustments are made. In order to eliminate short-term effects of business cycles, the series are smoothed using an 2 year moving average weighting scheme (longer weighting schemes do not substantially change the results). If data are available for a country, but these reflect short-run aberrations due to exogenous shocks (such as would be the case of beef sales in a country stricken with foot and mouth disease), these observations were dropped or "filtered" from the analysis.

Step 3. Filling in Missing Values

In some cases, data are available for countries on a sporadic basis. In other cases, data from a country may be available for only one year. From a Bayesian perspective, these observations should be given greatest weight in estimating missing years. Assuming that other factors are held constant, the missing years are extrapolated using changes and growth in aggregate national income. Based on the overriding philosophy of a long-run consumption function (defined earlier), countries which have missing data for any given year, are estimated based on historical dynamics of aggregate income for that country.

Step 4. Varying Parameter, Non-linear Estimation

Given the data available from the first three steps, the latent demand in additional countries is estimated using a “varying-parameter cross-sectionally pooled time series model”. Simply stated, the effect of income on latent demand is assumed to be constant across countries unless there is empirical evidence to suggest that this effect varies (i.e., the slope of the income effect is not necessarily same for all countries). This assumption applies across countries along the aggregate consumption function, but also over time (i.e., not all countries are perceived to have the same income growth prospects over time and this effect can vary from country to country as well). Another way of looking at this is to say that latent demand for 32 MW grid-connected PV solar photovoltaic systems is more likely to be similar across countries that have similar characteristics in terms of economic development (i.e., African countries will have similar latent demand structures controlling for the income variation across the pool of African countries).

This approach is useful across countries for which some notion of non-linearity exists in the aggregate cross-country consumption function. For some categories, however, the reader must realize that the numbers will reflect a country’s contribution to global latent demand and may never be realized in the form of local sales. For certain country-category combinations this will result in what at first glance will be odd results. For example, the latent demand for the category “space vehicles” will exist for “Togo” even though they have no space program. The assumption is that if the economies in these countries did not exist, the world aggregate for these categories would be lower. The share attributed to these countries is based on a proportion of their income (however small) being used to consume the category in question (i.e., perhaps via resellers).

Step 5. Fixed-Parameter Linear Estimation

Nonlinearities are assumed in cases where filtered data exist along the aggregate consumption function. Because the world consists of more than 200 countries, there will always be those countries, especially toward the bottom of the consumption function, where non-linear estimation is simply not possible. For these countries, equilibrium latent demand is assumed to be perfectly parametric and not a function of wealth (i.e., a country’s stock of income), but a function of current income (a country’s flow of income). In the long run, if a country has no current income, the latent demand for 32 MW grid-connected PV solar photovoltaic systems is assumed to approach zero. The assumption is that wealth stocks fall rapidly to zero if flow income falls to zero (i.e., countries which earn low levels of income will not use their savings, in the long run, to demand 32 MW grid-connected PV solar photovoltaic systems). In a graphical sense, for low income countries, latent demand approaches zero in a parametric linear fashion with a zero-zero intercept. In this stage of the estimation procedure, low-income countries are assumed to have a latent demand proportional to their income, based on the country closest to it on the aggregate consumption function.

Step 6. Aggregation and Benchmarking

Based on the models described above, latent demand figures are estimated for all countries of the world, including for the smallest economies. These are then aggregated to get world totals and regional totals. To make the numbers more meaningful, regional and global demand averages are presented. Figures are rounded, so minor inconsistencies may exist across tables.

Step 7. Latent Demand Density: Allocating Across Cities

With the advent of a “borderless world”, cities become a more important criteria in prioritizing markets, as opposed to regions, continents, or countries. This report also covers the world’s top 2000 cities. The purpose is to understand the density of demand within a country and the extent to which a city might be used as a point of distribution within its region. From an economic perspective, however, a city does not represent a population within rigid geographical boundaries. To an economist or strategic planner, a city represents an area of dominant influence over markets in adjacent areas. This influence varies from one industry to another, but also from one period of time to another.

Similar to country-level data, the reader needs to realize that latent demand allocated to a city may or may not represent real sales. For many items, latent demand is clearly observable in sales, as in the case for food or housing items. Consider, again, the category “satellite launch vehicles.” Clearly, there are no launch pads in most cities of the world. However, the core benefit of the vehicles (e.g. telecommunications, etc.) is "consumed" by residents or industries within the world's cities. Without certain cities, in other words, the world market for satellite launch vehicles would be lower for the world in general. One needs to allocate, therefore, a portion of the worldwide economic demand for launch vehicles to regions, countries and cities. This report takes the broader definition and considers, therefore, a city as a part of the global market. I allocate latent demand across areas of dominant influence based on the relative economic importance of cities within its home country, within its region and across the world total. Not all cities are estimated within each country as demand may be allocated to adjacent areas of influence. Since some cities have higher economic wealth than others within the same country, a city’s population is not generally used to allocate latent demand. Rather, the level of economic activity of the city vis-à-vis others.
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  • The 2016-2021 World Outlook for 32 MW Grid-Connected PV Solar Photovoltaic Systems
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